Bacterial Communities in Lanna Fermented Soybeans from Three Different Ethnolinguistic Groups in Northern Thailand.
Rujipas YongsawasAmmarin In-OnAngkana IntaJatupol KampuansaiHataichanok PandithNakarin SuwannarachSaisamorn LumyongThararat ChitovTerd DisayathanoowatPublished in: Microorganisms (2023)
Northern Thailand, the main part of the Lanna region, is home to a diverse range of ethnic groups, each with their own food and cultural heritage. The bacterial compositions in fermented soybean (FSB) products indigenous to three Lanna ethnolinguistic groups, including Karen, Lawa, and Shan, were investigated in this study. Bacterial DNA was extracted from the FSB samples and subjected to 16S rRNA gene sequencing using the Illumina sequencing platform. Metagenomic data showed that the predominant bacteria in all FSBs were members of the genus Bacillus (49.5-86.8%), and the Lawa FSB had the greatest bacterial diversity. The presence of genera Ignatzschineria , Yaniella , Atopostipes in the Karen and Lawa FSBs and Proteus in the Shan FSB might be indicators of food hygiene problems during processing. The network analysis predicted antagonistic effects of Bacillus against some indicator and pathogenic bacteria. The functional prediction revealed some potential functional properties of these FSBs. The presence of Bacillus in all FSBs and Vagococcus in the Shan FSB suggests that these FSBs could potentially be good sources of beneficial bacteria, and they should be conserved and promoted for health and food security reasons. However, food processing hygiene measures should be introduced and monitored to warrant their properties as health foods.
Keyphrases
- human health
- healthcare
- mental health
- network analysis
- single cell
- public health
- risk assessment
- gene expression
- bacillus subtilis
- health information
- high throughput
- genome wide
- copy number
- drinking water
- mass spectrometry
- cell free
- transcription factor
- global health
- machine learning
- microbial community
- electronic health record
- antibiotic resistance genes
- atomic force microscopy
- nucleic acid
- deep learning
- social media